{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import absolute_import, division, print_function, unicode_literals\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import metrics \n",
    "from tensorflow.keras.layers import Layer,Add, add,AveragePooling2D\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D ,Lambda ,GlobalAveragePooling2D,DepthwiseConv2D\n",
    "from tensorflow.keras.layers import concatenate, Add, BatchNormalization, Activation, PReLU, LeakyReLU ,Reshape,Multiply, multiply\n",
    "from tensorflow.keras import regularizers , Input ,Model\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from scipy import ndimage , misc\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 128\n",
    "epochs = 10\n",
    "IMAGE_HEIGHT = 32\n",
    "IMAGE_WIDTH = 32\n",
    "IMAGE_CHANNEL = 3\n",
    "QP = \"QP22\"\n",
    "total_train = 717720\n",
    "# total_valid = 62704\n",
    "# total_train = 312914\n",
    "total_valid = 62704"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_pickle(\"D:/TrainEnd2End/QP37.pkl\").head(32)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def training_data_generator(QTMTlabels, QTlabels_1, QTlabels_2, QTlabels_3, TTlabels, image_path):\n",
    "    # load in the image according to image path\n",
    "    #path = image_path\n",
    "    img = tf.io.read_file(image_path)\n",
    "    img = tf.image.decode_image(img, channels=3)\n",
    "    img = tf.image.convert_image_dtype(img, tf.float32)\n",
    "    img.set_shape([None, None, 3])\n",
    "    img = tf.image.resize(img, size=[IMAGE_HEIGHT, IMAGE_WIDTH])\n",
    "    img.set_shape([IMAGE_HEIGHT, IMAGE_WIDTH, IMAGE_CHANNEL])\n",
    "    QTMTlabels = tf.cast(QTMTlabels, tf.float32)\n",
    "    QTlabels_1 = tf.cast(QTlabels_1, tf.float32)\n",
    "    QTlabels_2 = tf.cast(QTlabels_2, tf.float32)\n",
    "    QTlabels_3 = tf.cast(QTlabels_3, tf.float32)\n",
    "    TTlabels = tf.cast(TTlabels, tf.int32)\n",
    "    #tf.stack([xcenter, ycenter, box_w, box_h, class_num], axis = 1)\n",
    "    return img, (QTMTlabels, QTlabels_1, QTlabels_2, QTlabels_3, TTlabels)# tuple\n",
    "\n",
    "def dataset_generator(filenames, batch_size, data_generator):\n",
    "    # load the training data into two NumPy arrays\n",
    "    df = pd.read_pickle(filenames)\n",
    "    QTMT = df['QTMT'].values\n",
    "    QT = df['QT'].values\n",
    "    TT = df['TT'].values\n",
    "    image = df['filepath'].values\n",
    "    QTMTlabels = []\n",
    "    QTlabels_1 = [] #complex\n",
    "    QTlabels_2 = [] #moderate\n",
    "    QTlabels_3 = [] #smooth\n",
    "    TTlabels = []\n",
    "    image_path = []\n",
    "    for i in range(len(QTMT)):\n",
    "\n",
    "        #QTMTlabels.append(QTMT[i])\n",
    "        image_path.append(image[i])\n",
    "        if(QTMT[i] == 0): #smooth\n",
    "            QTMTlabels.append([1,0,0])\n",
    "            if(QT[i] == 3 or QT[i] == 2):\n",
    "                QTlabels_1.append([0,1])\n",
    "                QTlabels_2.append([0,1])\n",
    "                QTlabels_3.append([0,1])\n",
    "            else:\n",
    "                QTlabels_1.append([1,0])\n",
    "                QTlabels_2.append([1,0])\n",
    "                QTlabels_3.append([1,0])\n",
    "        elif(QTMT[i] == 1):\n",
    "            QTMTlabels.append([0,1,0])\n",
    "            if(QT[i] == 3):\n",
    "                QTlabels_1.append([0,1])\n",
    "                QTlabels_2.append([0,1])\n",
    "                QTlabels_3.append([0,1])\n",
    "            else:\n",
    "                QTlabels_1.append([1,0])\n",
    "                QTlabels_2.append([1,0])\n",
    "                QTlabels_3.append([1,0])\n",
    "        elif(QTMT[i] == 2):\n",
    "            QTMTlabels.append([0,0,1])\n",
    "            if(QT[i] == 3):\n",
    "                QTlabels_1.append([0,1])\n",
    "                QTlabels_2.append([0,1])\n",
    "                QTlabels_3.append([0,1])\n",
    "            else:\n",
    "                QTlabels_1.append([1,0])\n",
    "                QTlabels_2.append([1,0])\n",
    "                QTlabels_3.append([1,0])\n",
    "        if (TT[i] == 0):\n",
    "            TTlabels.append([1,0])\n",
    "        else:\n",
    "            TTlabels.append([0,1])\n",
    "    #print(len(TTlabels))\n",
    "    QTMTlabels = np.asarray(QTMTlabels)\n",
    "    QTMTlabels = QTMTlabels.astype(np.int)\n",
    "    QTlabels_1 = np.asarray(QTlabels_1)\n",
    "    QTlabels_1 = QTlabels_1.astype(np.int)\n",
    "    QTlabels_2 = np.asarray(QTlabels_2)\n",
    "    QTlabels_2 = QTlabels_2.astype(np.int)\n",
    "    QTlabels_3 = np.asarray(QTlabels_3)\n",
    "    QTlabels_3 = QTlabels_3.astype(np.int)\n",
    "    TTlabels = np.asarray(TTlabels)\n",
    "    TTlabels = TTlabels.astype(np.int)\n",
    "    #image_path = df['filepath'].values\n",
    "    \n",
    "    # assume that each row of `features` corresponds to the same row as `labels`.\n",
    "    #assert QTMT.shape[0] == image_path.shape[0]\n",
    "    \n",
    "    dataset = tf.data.Dataset.from_tensor_slices((QTMTlabels,QTlabels_1,QTlabels_2,QTlabels_3,TTlabels,image_path))\n",
    "    dataset = dataset.map(data_generator, num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
    "    dataset = dataset.shuffle(100000).batch(batch_size, drop_remainder=True)\n",
    "    dataset = dataset.prefetch(buffer_size=tf.data.experimental.AUTOTUNE).repeat()\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<RepeatDataset shapes: ((128, 32, 32, 3), ((128, 3), (128, 2), (128, 2), (128, 2), (128, 2))), types: (tf.float32, (tf.float32, tf.float32, tf.float32, tf.float32, tf.int32))>\n"
     ]
    }
   ],
   "source": [
    "data_path = \"D:/TrainEnd2End/\"\n",
    "train_dataset = dataset_generator(data_path +QP+'.pkl', BATCH_SIZE, training_data_generator)\n",
    "print(train_dataset)\n",
    "# for a,b in train_dataset:\n",
    "#     print(a)\n",
    "#     print(b)\n",
    "\n",
    "\n",
    "#     break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def valid_data_generator(QTMTlabels, QTlabels_1, QTlabels_2, QTlabels_3, TTlabels, image_path):\n",
    "#def training_data_generator(QTMTlabels, image_path):\n",
    "    # load in the image according to image path\n",
    "    #path = image_path\n",
    "    img = tf.io.read_file(image_path)\n",
    "    img = tf.image.decode_image(img, channels=3)\n",
    "    img = tf.image.convert_image_dtype(img, tf.float32)\n",
    "    img.set_shape([None, None, 3])\n",
    "    img = tf.image.resize(img, size=[IMAGE_HEIGHT, IMAGE_WIDTH])\n",
    "    img.set_shape([IMAGE_HEIGHT, IMAGE_WIDTH, IMAGE_CHANNEL])\n",
    "    QTMTlabels = tf.cast(QTMTlabels, tf.float32)\n",
    "    QTlabels_1 = tf.cast(QTlabels_1, tf.float32)\n",
    "    QTlabels_2 = tf.cast(QTlabels_2, tf.float32)\n",
    "    QTlabels_3 = tf.cast(QTlabels_3, tf.float32)\n",
    "    TTlabels = tf.cast(TTlabels, tf.int32)\n",
    "    #tf.stack([xcenter, ycenter, box_w, box_h, class_num], axis = 1)\n",
    "    return img, (QTMTlabels, QTlabels_1, QTlabels_2, QTlabels_3, TTlabels)# tuple\n",
    "    #return img, QTMTlabels\n",
    "def test_dataset_generator(filenames, batch_size, data_generator):\n",
    "    # load the training data into two NumPy arrays\n",
    "    df = pd.read_pickle(filenames)\n",
    "    QTMT = df['QTMT'].values\n",
    "    QT = df['QT'].values\n",
    "    TT = df['TT'].values\n",
    "    image = df['filepath'].values\n",
    "    QTMTlabels = []\n",
    "    QTlabels_1 = [] #complex\n",
    "    QTlabels_2 = [] #moderate\n",
    "    QTlabels_3 = [] #smooth\n",
    "    TTlabels = []\n",
    "    image_path = []\n",
    "    for i in range(len(QTMT)):\n",
    "\n",
    "        #QTMTlabels.append(QTMT[i])\n",
    "        image_path.append(image[i])\n",
    "        if(QTMT[i] == 0): #smooth\n",
    "            QTMTlabels.append([1,0,0])\n",
    "            if(QT[i] == 3 or QT[i] == 2):\n",
    "                QTlabels_1.append([0,1])\n",
    "                QTlabels_2.append([0,1])\n",
    "                QTlabels_3.append([0,1])\n",
    "            else:\n",
    "                QTlabels_1.append([1,0])\n",
    "                QTlabels_2.append([1,0])\n",
    "                QTlabels_3.append([1,0])\n",
    "        elif(QTMT[i] == 1):\n",
    "            QTMTlabels.append([0,1,0])\n",
    "            if(QT[i] == 3):\n",
    "                QTlabels_1.append([0,1])\n",
    "                QTlabels_2.append([0,1])\n",
    "                QTlabels_3.append([0,1])\n",
    "            else:\n",
    "                QTlabels_1.append([1,0])\n",
    "                QTlabels_2.append([1,0])\n",
    "                QTlabels_3.append([1,0])\n",
    "        elif(QTMT[i] == 2):\n",
    "            QTMTlabels.append([0,0,1])\n",
    "            if(QT[i] == 3):\n",
    "                QTlabels_1.append([0,1])\n",
    "                QTlabels_2.append([0,1])\n",
    "                QTlabels_3.append([0,1])\n",
    "            else:\n",
    "                QTlabels_1.append([1,0])\n",
    "                QTlabels_2.append([1,0])\n",
    "                QTlabels_3.append([1,0])\n",
    "        if (TT[i] == 0):\n",
    "            TTlabels.append([1,0])\n",
    "        else:\n",
    "            TTlabels.append([0,1])\n",
    "    #print(len(TTlabels))\n",
    "    QTMTlabels = np.asarray(QTMTlabels)\n",
    "    QTMTlabels = QTMTlabels.astype(np.int)\n",
    "    QTlabels_1 = np.asarray(QTlabels_1)\n",
    "    QTlabels_1 = QTlabels_1.astype(np.int)\n",
    "    QTlabels_2 = np.asarray(QTlabels_2)\n",
    "    QTlabels_2 = QTlabels_2.astype(np.int)\n",
    "    QTlabels_3 = np.asarray(QTlabels_3)\n",
    "    QTlabels_3 = QTlabels_3.astype(np.int)\n",
    "    TTlabels = np.asarray(TTlabels)\n",
    "    TTlabels = TTlabels.astype(np.int)\n",
    "    \n",
    "    dataset = tf.data.Dataset.from_tensor_slices((QTMTlabels,QTlabels_1,QTlabels_2,QTlabels_3,TTlabels,image_path))\n",
    "    #dataset = tf.data.Dataset.from_tensor_slices((QTMTlabels,image_path))\n",
    "    dataset = dataset.map(data_generator, num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
    "    dataset = dataset.repeat().batch(batch_size, drop_remainder=True)\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<BatchDataset shapes: ((128, 32, 32, 3), ((128, 3), (128, 2), (128, 2), (128, 2), (128, 2))), types: (tf.float32, (tf.float32, tf.float32, tf.float32, tf.float32, tf.int32))>\n"
     ]
    }
   ],
   "source": [
    "data_path = \"D:/ValidEnd2End/valid_\"\n",
    "valid_dataset = test_dataset_generator(data_path +QP+'.pkl', BATCH_SIZE, valid_data_generator)\n",
    "print(valid_dataset)\n",
    "# for a,b in valid_dataset:\n",
    "#     print(a)\n",
    "#     print(b)\n",
    "\n",
    "\n",
    "#     break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _make_divisible(v, divisor, min_value=None):\n",
    "    if min_value is None:\n",
    "        min_value = divisor\n",
    "    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)\n",
    "    # Make sure that round down does not go down by more than 10%.\n",
    "    if new_v < 0.9 * v:\n",
    "        new_v += divisor\n",
    "    return new_v\n",
    "\n",
    "def swish(x):\n",
    "        \"\"\"Swish activation function: x * sigmoid(x).\n",
    "        Reference: [Searching for Activation Functions](https://arxiv.org/abs/1710.05941)\n",
    "        \"\"\"\n",
    "\n",
    "        return tf.nn.sigmoid(x) * x\n",
    "\n",
    "def MobileNetv2(input_shape, k, alpha=1.0):\n",
    "    \n",
    "    inputs_img = Input(shape=input_shape,name = 'Input0')\n",
    "\n",
    "    first_filters = _make_divisible(16 * alpha, 8)\n",
    "    '''conv0'''\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    x = Conv2D(16, (3,3), padding='same', strides=(1,1),name='conv0')(inputs_img)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN0')(x)\n",
    "    x = Activation(swish)(x) ##16\n",
    "    '''conv0'''\n",
    "    \n",
    "    '''_inverted_residual_block stage1 '''\n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 1\n",
    "    # Width\n",
    "    cchannel = int(16 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv1')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN1')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv2')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN2')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv3')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN3')(x)\n",
    "    #x = Add()([x, inputs])\n",
    "    ###################################\n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 1\n",
    "    # Width\n",
    "    cchannel = int(16 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv4')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN4')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv5')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN5')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv6')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN6')(x)\n",
    "    x = Add()([x, inputs])\n",
    "    '''_inverted_residual_block stage1'''\n",
    "    \n",
    "    x = Conv2D(16, (2,2), padding='same', strides=(2,2),name='conv7')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN7')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    '''_inverted_residual_block stage2'''\n",
    "    \n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(32 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv8')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN8')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv9')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN9')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv10')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN10')(x)\n",
    "    #x = Add()([x, inputs])\n",
    "    \n",
    "    ###################################\n",
    "    \n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(32 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv11')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN11')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv12')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN12')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv13')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN13')(x)\n",
    "    x = Add()([x, inputs])\n",
    "    \n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(32 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv14')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN14')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv15')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN15')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv16')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN16')(x)\n",
    "    x = Add()([x, inputs])\n",
    "    \n",
    "    '''_inverted_residual_block stage2'''\n",
    "    \n",
    "    x = Conv2D(32, (2,2), padding='same', strides=(2,2),name='conv17')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN17')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    '''_inverted_residual_block stage3'''\n",
    "    #1\n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(64 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv18')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN18')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv19')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN19')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv20')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN20')(x)\n",
    "    #x = Add()([x, inputs])\n",
    "    \n",
    "    ###################################\n",
    "    #2\n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(64 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv21')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN22')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv23')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN23')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv24')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN24')(x)\n",
    "    x = Add()([x, inputs])\n",
    "    #3\n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(64 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv25')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN25')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv26')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN26')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv27')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN27')(x)\n",
    "    x = Add()([x, inputs])\n",
    "    #4\n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(64 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv28')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN28')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv29')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN29')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv30')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN30')(x)\n",
    "    x = Add()([x, inputs])\n",
    "    \n",
    "    branch_2 = x\n",
    "    branch_3 = x\n",
    "    branch_4 = x\n",
    "    branch_tt = x\n",
    "    '''_inverted_residual_block stage3'''\n",
    "    \n",
    "    x = Conv2D(64, (2,2), padding='same', strides=(2,2),name='conv31')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN31')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    '''branch_tt'''\n",
    "\n",
    "    \n",
    "    '''branch_2'''\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(branch_2)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(96 * 1)\n",
    "    branch_2 = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='b2_conv0')(branch_2)\n",
    "    branch_2 = BatchNormalization(axis=channel_axis,name='b2_BN0')(branch_2)\n",
    "    branch_2 = Activation(swish)(branch_2)\n",
    "    \n",
    "    branch_2 = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'b2_conv1')(branch_2)\n",
    "    branch_2 = BatchNormalization(axis=channel_axis,name = 'b2_BN1')(branch_2)\n",
    "    branch_2 = Activation(swish)(branch_2)   \n",
    "    branch_2 = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='b2_conv2')(branch_2)\n",
    "    branch_2 = BatchNormalization(axis=channel_axis,name = 'b2_BN2')(branch_2)\n",
    "    \n",
    "    branch_2 = Conv2D(320, (1,1), padding='same', strides=(1,1),name='b2_conv3')(branch_2)\n",
    "    branch_2 = BatchNormalization(axis=channel_axis,name='b2_BN3')(branch_2)\n",
    "    branch_2 = Activation(swish)(branch_2)\n",
    "    branch_2 = GlobalAveragePooling2D()(branch_2)\n",
    "    branch_2 = Reshape((1, 1, 320))(branch_2)\n",
    "    branch_2 = Dropout(0.3, name='b2_Dropout')(branch_2)\n",
    "    branch_2 = Conv2D(2, (1, 1), padding='same',name = 'b2_conv4')(branch_2)\n",
    "    branch_2 = Activation('softmax', name='b2_softmax')(branch_2)\n",
    "    b2_output = Reshape((2,),name = \"QT_complex\")(branch_2)\n",
    "\n",
    "    \n",
    "\n",
    "    \n",
    "    '''branch_2'''\n",
    "\n",
    "\n",
    "    '''branch_3'''\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(branch_3)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(96 * 1)\n",
    "    branch_3 = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='b3_conv0')(branch_3)\n",
    "    branch_3 = BatchNormalization(axis=channel_axis,name='b3_BN0')(branch_3)\n",
    "    branch_3 = Activation(swish)(branch_3)\n",
    "    \n",
    "    branch_3 = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'b3_conv1')(branch_3)\n",
    "    branch_3 = BatchNormalization(axis=channel_axis,name = 'b3_BN1')(branch_3)\n",
    "    branch_3 = Activation(swish)(branch_3)   \n",
    "    branch_3 = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='b3_conv2')(branch_3)\n",
    "    branch_3 = BatchNormalization(axis=channel_axis,name = 'b3_BN2')(branch_3)\n",
    "    \n",
    "    branch_3 = Conv2D(320, (1,1), padding='same', strides=(1,1),name='b3_conv3')(branch_3)\n",
    "    branch_3 = BatchNormalization(axis=channel_axis,name='b3_BN3')(branch_3)\n",
    "    branch_3 = Activation(swish)(branch_3)\n",
    "    branch_3 = GlobalAveragePooling2D()(branch_3)\n",
    "    branch_3 = Reshape((1, 1, 320))(branch_3)\n",
    "    branch_3 = Dropout(0.3, name='b3_Dropout')(branch_3)\n",
    "    branch_3 = Conv2D(2, (1, 1), padding='same',name = 'b3_conv4')(branch_3)\n",
    "    branch_3 = Activation('softmax', name='b3_softmax')(branch_3)\n",
    "    b3_output = Reshape((2,),name = \"QT_moderate\")(branch_3)\n",
    "\n",
    "\n",
    "    '''branch_3'''\n",
    "    \n",
    "    '''bracnch4'''\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(96 * 1)\n",
    "    branch_4 = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='b4_conv0')(branch_4)\n",
    "    branch_4 = BatchNormalization(axis=channel_axis,name='b4_BN0')(branch_4)\n",
    "    branch_4 = Activation(swish)(branch_4)\n",
    "    \n",
    "    branch_4 = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'b4_conv1')(branch_4)\n",
    "    branch_4 = BatchNormalization(axis=channel_axis,name = 'b4_BN1')(branch_4)\n",
    "    branch_4 = Activation(swish)(branch_4)   \n",
    "    branch_4 = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='b4_conv2')(branch_4)\n",
    "    branch_4 = BatchNormalization(axis=channel_axis,name = 'b4_BN2')(branch_4)\n",
    "    \n",
    "    branch_4 = Conv2D(320, (1,1), padding='same', strides=(1,1),name='b4_conv3')(branch_4)\n",
    "    branch_4 = BatchNormalization(axis=channel_axis,name='b4_BN3')(branch_4)\n",
    "    branch_4 = Activation(swish)(branch_4)\n",
    "    branch_4 = GlobalAveragePooling2D()(branch_4)\n",
    "    branch_4 = Reshape((1, 1, 320))(branch_4)\n",
    "    branch_4 = Dropout(0.3, name='b4_Dropout')(branch_4)\n",
    "    branch_4 = Conv2D(2, (1, 1), padding='same',name = 'b4_conv4')(branch_4)\n",
    "    branch_4 = Activation('softmax', name='b4_softmax')(branch_4)\n",
    "    b4_output = Reshape((2,),name = \"QT_smooth\")(branch_4)\n",
    "\n",
    "    \n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(branch_tt)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(96 * 1)\n",
    "    branch_tt = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='b5_conv0')(branch_tt)\n",
    "    branch_tt = BatchNormalization(axis=channel_axis,name='b5_BN0')(branch_tt)\n",
    "    branch_tt = Activation(swish)(branch_tt)\n",
    "    \n",
    "    branch_tt = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'b5_conv1')(branch_tt)\n",
    "    branch_tt = BatchNormalization(axis=channel_axis,name = 'b5_BN1')(branch_tt)\n",
    "    branch_tt = Activation(swish)(branch_tt)   \n",
    "    branch_tt = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='b5_conv2')(branch_tt)\n",
    "    branch_tt = BatchNormalization(axis=channel_axis,name = 'b5_BN2')(branch_tt)\n",
    "    \n",
    "    branch_tt = Conv2D(320, (1,1), padding='same', strides=(1,1),name='b5_conv3')(branch_tt)\n",
    "    branch_tt = BatchNormalization(axis=channel_axis,name='b5_BN3')(branch_tt)\n",
    "    branch_tt = Activation(swish)(branch_tt)\n",
    "    branch_tt = GlobalAveragePooling2D()(branch_tt)\n",
    "    branch_tt = Reshape((1, 1, 320))(branch_tt)\n",
    "    branch_tt = Dropout(0.3, name='b5_Dropout')(branch_tt)\n",
    "    branch_tt = Conv2D(2, (1, 1), padding='same',name = 'b5_conv4')(branch_tt)\n",
    "    branch_tt = Activation('softmax', name='b5_softmax')(branch_tt)\n",
    "    b5_output = Reshape((2,),name = \"TT\")(branch_tt)\n",
    "    \n",
    "    '''_inverted_residual_block stage4'''\n",
    "    #1\n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(96 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv32')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN32')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv33')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN33')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv34')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN34')(x)\n",
    "    #x = Add()([x, inputs])\n",
    "    ###################################\n",
    "    #2\n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(96 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv35')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN35')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv36')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN36')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv37')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN37')(x)\n",
    "    x = Add()([x, inputs])\n",
    "    \n",
    "    '''_inverted_residual_block stage4'''\n",
    "    \n",
    "    '''Final Stage'''\n",
    "    \n",
    "    if alpha > 1.0:\n",
    "        last_filters = _make_divisible(320 * alpha, 8)\n",
    "    else:\n",
    "        last_filters = 320\n",
    "\n",
    "        \n",
    "    x = Conv2D(320, (1,1), padding='same', strides=(1,1),name='conv38')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN38')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    x = GlobalAveragePooling2D()(x)\n",
    "    x = Reshape((1, 1, last_filters))(x)\n",
    "    x = Dropout(0.3, name='Dropout')(x)\n",
    "    x = Conv2D(k, (1, 1), padding='same',name = 'conv39')(x)\n",
    "    x = Activation('softmax', name='softmax')(x)\n",
    "    output = Reshape((k,),name = \"QTMT\")(x)\n",
    "\n",
    "    model = Model(inputs_img,[output,b2_output,b3_output,b4_output,b5_output])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = MobileNetv2((32, 32, 3), 3, 1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "QTMT_pred = tf.constant(0, shape=(BATCH_SIZE, 3),dtype = tf.float32)\n",
    "def conditional_loss_QTMT(y_true, y_pred):\n",
    "    global QTMT_pred\n",
    "    '''QT''' \n",
    "    QTMT_pred = y_pred\n",
    "    return tf.keras.losses.categorical_crossentropy(y_true, y_pred)\n",
    "    \n",
    "def conditional_loss_complex(y_true, y_pred):\n",
    "    global QTMT_pred\n",
    "    '''QT''' \n",
    "    #print(QTMT_pred)\n",
    "    #print(y_pred)\n",
    "    #print(tf.keras.backend.argmax(model.output[0],axis=-1))\n",
    "    loss = tf.keras.losses.categorical_crossentropy(y_true, y_pred)\n",
    "    check = tf.constant(2, shape=(BATCH_SIZE, ),dtype = tf.int64)\n",
    "\n",
    "    mask = tf.math.equal(tf.keras.backend.argmax(QTMT_pred, axis=-1), check)\n",
    "    mask = tf.cast(mask, tf.float32)\n",
    "    return mask * tf.keras.losses.categorical_crossentropy(y_true, y_pred)\n",
    "\n",
    "\n",
    "def conditional_loss_moderate(y_true, y_pred):\n",
    "    global QTMT_pred\n",
    "    '''QT''' \n",
    "    loss = tf.keras.losses.categorical_crossentropy(y_true, y_pred)\n",
    "    check = tf.constant(1, shape=(BATCH_SIZE, ),dtype = tf.int64)\n",
    "\n",
    "    mask = tf.math.equal(tf.keras.backend.argmax(QTMT_pred, axis=-1), check)\n",
    "    mask = tf.cast(mask, tf.float32)\n",
    "    return mask * tf.keras.losses.categorical_crossentropy(y_true, y_pred)\n",
    "\n",
    "\n",
    "def conditional_loss_smooth(y_true, y_pred):\n",
    "    global QTMT_pred\n",
    "    '''QT''' \n",
    "    loss = tf.keras.losses.categorical_crossentropy(y_true, y_pred)\n",
    "    check = tf.constant(0, shape=(BATCH_SIZE, ),dtype = tf.int64)\n",
    "\n",
    "    mask = tf.math.equal(tf.keras.backend.argmax(QTMT_pred, axis=-1), check)\n",
    "    mask = tf.cast(mask, tf.float32)\n",
    "    return mask * tf.keras.losses.categorical_crossentropy(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "Adam = tf.keras.optimizers.Adam(learning_rate=0.0001)\n",
    "#sgd = tf.keras.optimizers.SGD(lr=0.0001, momentum=0.9, nesterov=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "def lr_scheduler(epoch):\n",
    "    if epoch % 10 == 0:\n",
    "        tf.keras.backend.set_value(model.optimizer.lr, model.optimizer.lr * 0.3)\n",
    "    return model.optimizer.lr\n",
    "change_lr = tf.keras.callbacks.LearningRateScheduler(lr_scheduler)\n",
    "'''\n",
    "\n",
    "reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_accuracy', factor=0.5,patience=3, min_lr=0.00001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "losses = {\"QTMT\" : conditional_loss_QTMT,\n",
    "          \"QT_complex\" : conditional_loss_complex,\n",
    "          \"QT_moderate\" : conditional_loss_moderate,\n",
    "          \"QT_smooth\" : conditional_loss_smooth,\n",
    "          \"TT\" : \"categorical_crossentropy\",\n",
    "         \n",
    "         }\n",
    "model.compile(optimizer=Adam,\n",
    "               loss=losses,\n",
    "               metrics=['accuracy'])\n",
    "# model.compile(optimizer=Adam,\n",
    "#               loss=[categorical_focal_loss(alpha=.25, gamma=2)],\n",
    "#               metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint_path = \"ckpt_End2End_cond/\"+QP+\"_{epoch:02d}.h5\"\n",
    "#CKP_DIR_SAVE_CALLBACKS = './checkpoint/ckpt_QP37_mobile_b2_3class/best_weight_branch2.h5'\n",
    "#checkpoint_dir = os.path.dirname(CKP_DIR_SAVE_CALLBACKS)\n",
    "#checkpoint_dir = os.path.dirname(checkpoint_path)\n",
    "\n",
    "# Create a callback that saves the model's weights\n",
    "cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,                                   \n",
    "                                                 save_weights_only=True,\n",
    "                                                 save_best_only=False,\n",
    "                                                 monitor='val_accuracy',\n",
    "                                                 verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model.fit(train_dataset, epochs=1, validation_data=None, verbose=1)\n",
    "#tf.config.experimental_run_functions_eagerly(True)\n",
    "history = model.fit_generator(\n",
    "    train_dataset,\n",
    "    steps_per_epoch=total_train // BATCH_SIZE,\n",
    "    epochs=epochs,\n",
    "    validation_data=valid_dataset,\n",
    "    validation_steps=total_valid // BATCH_SIZE,\n",
    "    callbacks=[cp_callback],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "checkpoint_path = \"./checkpoint/ckpt_QP22_mobile/best_weight_branch1.h5\"\n",
    "model.load_weights(checkpoint_path,by_name=True) \n",
    "checkpoint_path = \"./checkpoint/ckpt_QP22_mobile_b2/best_weight_branch2.h5\"\n",
    "model.load_weights(checkpoint_path,by_name=True) \n",
    "checkpoint_path = \"./checkpoint/ckpt_QP22_mobile_b3/best_weight_branch3.h5\"\n",
    "model.load_weights(checkpoint_path,by_name=True) \n",
    "checkpoint_path = \"./checkpoint/ckpt_QP22_mobile_b4/best_weight_branch4.h5\"\n",
    "model.load_weights(checkpoint_path,by_name=True) \n",
    "checkpoint_path = \"./checkpoint/ckpt_QP22_mobile_tt/best_weight_branch_tt.h5\"\n",
    "model.load_weights(checkpoint_path,by_name=True) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "import math\n",
    "import os\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath = \"D:/frame/\"\n",
    "#outpath = \"D:/VCCResearch/Frame_predict/\"\n",
    "seq_name = \"Traffic\"\n",
    "QP = \"QP22\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def frame_processing(filename,currframe = 0):\n",
    "    f = open(\"D:/frame/\"+filename+\"/\"+seq_name+'%d'%currframe+'.txt', \"r\")\n",
    "    rowlist = []\n",
    "    for line in f:\n",
    "        rowpix = line.split(\" \")\n",
    "        results = [int(i) for i in rowpix[:-1]]\n",
    "        rowlist.append(results)\n",
    "    img = np.asarray(rowlist)\n",
    "    img = img /4\n",
    "    return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def frame_processing(filename,currframe = 0):\n",
    "    f = open(\"D:/frame/\"+filename+\"/\"+seq_name+'%d'%currframe+'.txt', \"r\")\n",
    "\n",
    "    rowlist = []\n",
    "    for line in f:\n",
    "        rowpix = line.split(\" \")\n",
    "        results = [int(i) for i in rowpix[:-1]]\n",
    "        rowlist.append(results)\n",
    "    img = np.asarray(rowlist)\n",
    "    img = img /4\n",
    "    return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "origin frame:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1c296ca9a58>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = frame_processing(seq_name,0)\n",
    "yuv_img = Image.fromarray(img.astype('uint8')).convert('YCbCr')\n",
    "\n",
    "print(\"origin frame:\")\n",
    "plt.imshow(yuv_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def frame_padding(frame,CTU_size = 128):\n",
    "    width , height = frame.shape[1],frame.shape[0]\n",
    "    #print(width,height)\n",
    "    width_CTU_nums = math.ceil(width/128)\n",
    "    height_CTU_nums = math.ceil(height/128)\n",
    "    pad_frame = np.zeros((height_CTU_nums*128,width_CTU_nums*128))\n",
    "    for x in range(height):\n",
    "        for y in range(width):\n",
    "            pad_frame[x][y] = frame[x][y]\n",
    "    return pad_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "padding frame:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1c28fa05fd0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yuv_img_padding = frame_padding(img)\n",
    "print(\"padding frame:\")\n",
    "yuv_img_padding = Image.fromarray(yuv_img_padding.astype('uint8')).convert('YCbCr')\n",
    "plt.imshow(yuv_img_padding)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BackBone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "seq_list = [\"Tango2\",\"FoodMarket4\",\"Campfire\",\"CatRobot1\",\"DaylightRoad2\",\"ParkRunning3\",\"MarketPlace\",\"RitualDance\",\n",
    "            \"Cactus\",\"BasketballDrive\",\"BQTerrace\",\"RaceHorsesC\",\"BQMall\",\"PartyScene\",\"BasketballDrill\",\"RaceHorses\",\n",
    "            \"BQSquare\",\"BlowingBubbles\",\"BasketballPass\",\"FourPeople\",\"Johnny\",\"KristenAndSara\"\n",
    "            ]\n",
    "QPList = [\"QP22\",\"QP27\",\"QP32\",\"QP37\"]\n",
    "\n",
    "def gen_range(yuv_img,yuv_img_padding,currframe = 0,index = 0,rd_mataining={}):\n",
    "    org_width,org_height = yuv_img.size\n",
    "    width , height = yuv_img_padding.size\n",
    "    \n",
    "    InFirstCTU = 0\n",
    "    with open(prediction_path+'frame'+str(currframe)+'.txt', 'w') as f:\n",
    "        for blocky in range(0, height, 128):\n",
    "            for blockx in range(0, width, 128):\n",
    "                for beginy in range(blocky, blocky+128, 32):\n",
    "                    for beginx in range(blockx, blockx+128, 32):\n",
    "                        if beginx > org_width or beginx + 32 > org_width or beginy > org_height or beginy + 32 > org_height:\n",
    "                            continue\n",
    "                        val = \"\"\n",
    "                        val += str(beginx)+\" \"+str(beginy)+\" \"+str(beginx+32)+\" \"+str(beginy+32)+\" \"\n",
    "                        item = pred_all[index]\n",
    "                        #print(item)\n",
    "                        #InFirstCTU+=1\n",
    "                        \n",
    "                        for ele in rd_mataining[item]:\n",
    "                            val += str(ele)+\" \"\n",
    "                            \n",
    "\n",
    "                        f.write(\"%s\\n\" % (val))\n",
    "                        index+=1\n",
    "                        InFirstCTU+=1\n",
    "    return index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for QP in QPList:\n",
    "    for seq_name in seq_list:\n",
    "        pred_all = []\n",
    "        checkpoint_path = \"./checkpoint/ckpt_\"+QP+\"_mobile/best_weight_branch1.h5\"\n",
    "        model.load_weights(checkpoint_path,by_name=True)       \n",
    "        test_path = \"/\"+QP+\"_block\"\n",
    "        idx = 0\n",
    "\n",
    "        for i in range(1):\n",
    "\n",
    "            predict_label = []\n",
    "            test_dir = \"D:/frame/\"+seq_name+test_path+\"/test\"+str(i)\n",
    "            print(test_dir)\n",
    "            test_image_generator = ImageDataGenerator(rescale = 1./255)\n",
    "            test_data_gen = test_image_generator.flow_from_directory(batch_size=1,\n",
    "                                                                          directory=test_dir,\n",
    "                                                                          target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
    "                                                                          shuffle=False,\n",
    "                                                                          class_mode=None)\n",
    "\n",
    "            prediction=model.predict_generator(test_data_gen,verbose=1)\n",
    "\n",
    "\n",
    "\n",
    "            predict_label=np.argmax(prediction,axis=1)\n",
    "            for label in predict_label:\n",
    "                pred_all.append(label)\n",
    "\n",
    "                \n",
    "        rd_mataining = {0:[2,2,3,0],1:[2,4,4,0],2:[2,5,4,2]} #(5-6,7-8,9-10)\n",
    "        #prediction_path = \"D:/frame/\"+seq_name+\"/Prediction/\"+QP+\"/\"\n",
    "        prediction_path = \"D:/frame/\"+seq_name+\"/re_pred/\"+QP+\"/\"\n",
    "        index = 0\n",
    "        for i in range(1):\n",
    "            img = frame_processing(seq_name,i)\n",
    "            yuv_img = Image.fromarray(img.astype('uint8')).convert('YCbCr')\n",
    "            yuv_img_padding = frame_padding(img)\n",
    "            yuv_img_padding = Image.fromarray(yuv_img_padding.astype('uint8')).convert('YCbCr')\n",
    "            index = gen_range(yuv_img,yuv_img_padding,i,index,rd_mataining)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BackBone & Three Branch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "seq_list = [\"Tango2\",\"FoodMarket4\",\"Campfire\",\"CatRobot1\",\"DaylightRoad2\",\"ParkRunning3\",\"MarketPlace\",\"RitualDance\",\n",
    "             \"Cactus\",\"BasketballDrive\",\"BQTerrace\",\"ParkScene\",\"RaceHorsesC\",\"BQMall\",\"PartyScene\",\"BasketballDrill\",\"RaceHorses\",\n",
    "             \"BQSquare\",\"BlowingBubbles\",\"BasketballPass\",\"FourPeople\",\"Johnny\",\"KristenAndSara\"\n",
    "             ]\n",
    "\n",
    "QPList = [\"QP22\",\"QP27\",\"QP32\",\"QP37\"]\n",
    "\n",
    "def gen_range(yuv_img,yuv_img_padding,currframe = 0,index = 0,rd_mataining={}):\n",
    "    org_width,org_height = yuv_img.size\n",
    "    width , height = yuv_img_padding.size\n",
    "    \n",
    "    InFirstCTU = 0\n",
    "    with open(prediction_path+'frame'+str(currframe)+'.txt', 'w') as f:\n",
    "        for blocky in range(0, height, 128):\n",
    "            for blockx in range(0, width, 128):\n",
    "                for beginy in range(blocky, blocky+128, 32):\n",
    "                    for beginx in range(blockx, blockx+128, 32):\n",
    "                        if beginx > org_width or beginx + 32 > org_width or beginy > org_height or beginy + 32 > org_height:\n",
    "                            continue\n",
    "                        val = \"\"\n",
    "                        val += str(beginx)+\" \"+str(beginy)+\" \"+str(beginx+32)+\" \"+str(beginy+32)+\" \"\n",
    "                        item = pred_all[index]\n",
    "                        #print(item)\n",
    "                        #InFirstCTU+=1\n",
    "                        if index in candidate_idx:\n",
    "\n",
    "                            #print(candidate_idx)\n",
    "                            qtitem = pred_conf_label[index]\n",
    "\n",
    "                            if qtitem == 0:\n",
    "                                pred_all[index] = 3\n",
    "                                #print('in')\n",
    "                                for ele in rd_mataining[3]:\n",
    "                                    val += str(ele)+\" \"\n",
    "\n",
    "                            else:\n",
    "                                pred_all[index] = 4\n",
    "                                for ele in rd_mataining[4]:\n",
    "                                    val += str(ele)+\" \"\n",
    "                        elif index in candidate_idx_mid:\n",
    "\n",
    "                            qtitem = pred_conf_label_mid[index]\n",
    "\n",
    "                            if qtitem == 0:\n",
    "                                pred_all[index] = 5\n",
    "                                #print('in')\n",
    "                                for ele in rd_mataining[5]:\n",
    "                                    val += str(ele)+\" \"\n",
    "                                \n",
    "\n",
    "                            else:\n",
    "                                pred_all[index] = 6\n",
    "                                #print('in')\n",
    "                                for ele in rd_mataining[6]:\n",
    "                                    val += str(ele)+\" \"\n",
    "                                    \n",
    "                        elif index in candidate_idx_QT12:\n",
    "\n",
    "                            qtitem = pred_conf_label_QT12[index]\n",
    "\n",
    "                            if qtitem == 0:\n",
    "                                pred_all[index] = 7\n",
    "                                #print('in')\n",
    "                                for ele in rd_mataining[7]:\n",
    "                                    val += str(ele)+\" \"\n",
    "                                \n",
    "\n",
    "                            else:\n",
    "                                pred_all[index] = 8\n",
    "                                #print('in')\n",
    "                                for ele in rd_mataining[8]:\n",
    "                                    val += str(ele)+\" \"\n",
    "                        else:\n",
    "                            for ele in rd_mataining[item]:\n",
    "                                val += str(ele)+\" \"\n",
    "                        #print(pred_all_tt)\n",
    "                        if pred_all_tt[index] == 0:\n",
    "                            val += \"1\"\n",
    "                        else :\n",
    "                            val += \"0\"\n",
    "                        f.write(\"%s\\n\" % (val))\n",
    "                        index+=1\n",
    "                        InFirstCTU+=1\n",
    "    return index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for QP in QPList:\n",
    "    for seq_name in seq_list:\n",
    "        pred_all = []\n",
    "        pred_all_tt = []\n",
    "        checkpoint_path = \"./checkpoint/ckpt_\"+QP+\"_mobile/best_weight_branch1.h5\"\n",
    "        model.load_weights(checkpoint_path,by_name=True) \n",
    "        checkpoint_path = \"./checkpoint/ckpt_\"+QP+\"_mobile_b2/best_weight_branch2.h5\"\n",
    "        model.load_weights(checkpoint_path,by_name=True) \n",
    "        checkpoint_path = \"./checkpoint/ckpt_\"+QP+\"_mobile_b3/best_weight_branch3.h5\"\n",
    "        model.load_weights(checkpoint_path,by_name=True) \n",
    "        checkpoint_path = \"./checkpoint/ckpt_\"+QP+\"_mobile_b4/best_weight_branch4.h5\"\n",
    "        model.load_weights(checkpoint_path,by_name=True) \n",
    "        checkpoint_path = \"./checkpoint/ckpt_\"+QP+\"_mobile_tt/best_weight_branch_tt.h5\"\n",
    "        model.load_weights(checkpoint_path,by_name=True) \n",
    "\n",
    "        candidate_idx = []\n",
    "        pred_conf_label = []\n",
    "        candidate_idx_mid = []\n",
    "        pred_conf_label_mid = []\n",
    "        candidate_idx_QT12 = []\n",
    "        pred_conf_label_QT12 = []\n",
    "        \n",
    "        Threshold_TT = 0.5\n",
    "        Threshold_QT2 = 0.6\n",
    "        Threshold_QT3 = 0.6\n",
    "        Threshold_QT12 = 0.5\n",
    "        Threshold_QT78 = 0.5\n",
    "        \n",
    "        test_path = \"/\"+QP+\"_block\"\n",
    "        idx = 0\n",
    "        idx_mid = 0\n",
    "        idx_QT12 = 0\n",
    "        idx_tt = 0\n",
    "        for i in range(13):\n",
    "\n",
    "            predict_label = []\n",
    "            test_dir = \"D:/frame/\"+seq_name+test_path+\"/test\"+str(i)\n",
    "            print(test_dir)\n",
    "            test_image_generator = ImageDataGenerator(rescale = 1./255)\n",
    "            test_data_gen = test_image_generator.flow_from_directory(batch_size=201,\n",
    "                                                                          directory=test_dir,\n",
    "                                                                          target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
    "                                                                          shuffle=False,\n",
    "                                                                          class_mode=None)\n",
    "            #with tf.device('/cpu:0'):\n",
    "            prediction,pred_QT,pred_QT_mid,pred_QT12,pred_tt=model.predict_generator(test_data_gen,verbose=1)\n",
    "\n",
    "\n",
    "\n",
    "            predict_label=np.argmax(prediction,axis=1)\n",
    "            ###\n",
    "            predict_label_tt=np.argmax(pred_tt,axis=1)\n",
    "            for label in predict_label_tt:\n",
    "                pred_all_tt.append(label)\n",
    "            #print(pred_all_tt)\n",
    "            ###\n",
    "            for label in predict_label:\n",
    "                pred_all.append(label)\n",
    "\n",
    "            pred_QT_label = np.argmax(pred_QT, axis=1)\n",
    "            pred_QT_mid_label = np.argmax(pred_QT_mid, axis=1)#\n",
    "            pred_QT12_label = np.argmax(pred_QT12, axis=1)#\n",
    "            #print(pred_QT12_label)\n",
    "            \n",
    "            ################  TT branch  ###############\n",
    "  \n",
    "            for pred in pred_tt[:]:\n",
    "                if np.amax(pred, axis=0) < Threshold_TT and pred_all_tt[idx_tt] == 1:\n",
    "                    pred_all_tt[idx_tt] = 0\n",
    "                \n",
    "                idx_tt+=1\n",
    "            ################  TT branch  ###############\n",
    "            \n",
    "            \n",
    "            ################  Depth910 QT 23 branch  ###############\n",
    "            for label in pred_QT_label:\n",
    "                pred_conf_label.append(label)\n",
    "            for pred in pred_QT[:]:\n",
    "                if((np.amax(pred, axis=0) >= Threshold_QT2 and pred_conf_label[idx] == 0)and pred_all[idx] == 2):\n",
    "                    candidate_idx.append(idx)\n",
    "                elif((np.amax(pred, axis=0) >= Threshold_QT3 and pred_conf_label[idx] == 1)and pred_all[idx] == 2):\n",
    "                    candidate_idx.append(idx)\n",
    "                idx+=1\n",
    "            ################  Depth78 QT 23 branch  ###############\n",
    "            for label in pred_QT_mid_label:\n",
    "                pred_conf_label_mid.append(label)\n",
    "            for pred in pred_QT_mid[:]:\n",
    "                if(np.amax(pred, axis=0) >= Threshold_QT78 and pred_all[idx_mid] == 1):\n",
    "                    candidate_idx_mid.append(idx_mid)\n",
    "                idx_mid+=1\n",
    "             ################   QT 12 branch  ###############\n",
    "            for label in pred_QT12_label:  \n",
    "                pred_conf_label_QT12.append(label)\n",
    "            for pred in pred_QT12[:]:\n",
    "                if(np.amax(pred, axis=0) >= Threshold_QT12 and pred_all[idx_QT12] == 0):\n",
    "                    candidate_idx_QT12.append(idx_QT12)\n",
    "                idx_QT12+=1\n",
    "                \n",
    "        rd_mataining = {0:[2,2,3,0],1:[2,4,4,0],2:[2,5,4,2],3:[2,5,2,5],4:[3,4,4,2],5:[2,4,2,4],6:[3,2,4,0],7:[2,1,2,1],8:[2,2,3,0]} #(5-6,7-8,9-10)\n",
    "        prediction_path = \"D:/frame/\"+seq_name+\"/Prediction/\"+QP+\"/\"\n",
    "        #prediction_path = \"D:/frame/\"+seq_name+\"/Pred2/\"+QP+\"/\"\n",
    "        index = 0\n",
    "        for i in range(13):\n",
    "            img = frame_processing(seq_name,i)\n",
    "            yuv_img = Image.fromarray(img.astype('uint8')).convert('YCbCr')\n",
    "            yuv_img_padding = frame_padding(img)\n",
    "            yuv_img_padding = Image.fromarray(yuv_img_padding.astype('uint8')).convert('YCbCr')\n",
    "            index = gen_range(yuv_img,yuv_img_padding,i,index,rd_mataining)"
   ]
  }
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